Methods for orientation and tilt identification of photovoltaic systems and solar irradiance sensors

ABSTRACT

The present invention relates to methods and systems for identifying PV system and solar irradiance sensor orientation and tilt based on energy production, energy received, simulated energy production, estimated energy received, production skew, and energy received skew. The present invention relates to systems and methods for detecting orientation and tilt of a PV system based on energy production and simulated energy production; for detecting the orientation and tilt of a solar irradiance sensor based on solar irradiance observation and simulated solar irradiance observation; for detecting orientation of a PV system based on energy production and energy production skew; and for detecting orientation of a solar irradiance sensor based on solar irradiance observation and solar irradiance observation skew.

This application is a divisional of Ser. No. 13/681,803 filed Nov. 20,2012, which is a continuation-in part of application Ser. No. 12/77,235filed May 10, 2010 and claims priority to provisional patent applicationNo. 61/576,313 filed Dec. 15, 2011 entitled “Methods for Orientation andTilt Identification of Photovoltaic Systems and Solar IrradianceSensors,” and provisional patent application No. 61/576,315 filed Dec.15, 2011 entitled, “Methods for Location Identification of RenewableEnergy Systems and Environmental Sensors from Energy Production andSensor Measurements” all of which are incorporated herein by reference.

One of the greatest obstacles to adoption of distributed photovoltaicprojects is the ability to efficiently monitor and analyze a fleet ofsystems and solar irradiance sensors. There currently exist severalmonitoring solutions with analytics based on solar resource received.Solar irradiance sensors, such as pyranometers or reference cells, areplaced at a location with or without a PV system to measure solarirradiance received. PV system and solar irradiance sensor orientation,the direction at which the device is angled, and tilt, the angle atwhich the device is raised, are major determinants of solar energyreceived and consequentially energy produced by PV systems. While solarirradiance based analytics have proven effective in monitoring,orientation and tilt among other variables describing a PV system orsolar irradiance sensor are user inputs and subject to human error. Whensystem parameters are incorrectly identified it is difficult to employanalytical algorithms reliant upon solar irradiance due to the errorscaused by incorrect amounts of solar resource received. It is difficultto accurately monitor and analyze a fleet of projects with these errors,thus the need for systems and methods for correctly identifying theorientation and tilt of a PV system and solar irradiance sensors.

In order to efficiently manage and operate PV projects, energyproduction must be monitored and analyzed. Several solutions to thisproblem currently exist, employing monitoring hardware on location andrunning analytical algorithms on the data. As solar irradiance is thedriver of PV energy production, some of these analytical algorithms arebased on solar resource received by a system. Solar irradiance reachinga PV system or a solar irradiance sensor is determined by location,orientation, and tilt of the hardware among other variables and theanalytical algorithms leverage this information to evaluate PV systemperformance. The process of deploying PV systems and solar irradiancesensors in the field involves several parties including OEMs,financiers, and distributors among others. Many of these parties haveinterest in monitoring their systems, although they may be severallayers away from the end user. Due to the separation, interested partiesmay have partially complete or incorrect information about thesesystems. This separation can limit the benefits associated withmonitoring and analyzing PV projects, thus the need for methodology toaccurately identify the orientation and tilt of the hardware used in theprojects. This patent describes the methodologies for identifyingorientation and tilt of PV systems based on energy production andorientation and tilt of solar irradiance sensors based on the solarirradiance observed. A correlation based methodology leverages a PVproduction model and iterates through orientation and tilt in order toidentify a system's orientation and tilt. A correlation basedmethodology leverages solar irradiance models and iterates throughorientation and tilt in order to identify a solar irradiance sensor'sorientation and tilt. An energy production skew based methodologyleverages differences between morning and evening production in order toidentify a system's tilt. A measured solar irradiance skew basedmethodology leverages differences between morning and evening observedsolar irradiance in order to identify a sensor's tilt.

These and other features, aspects and advantages of the presentinvention will become better understood with reference to the followingdescription and claims.

SUMMARY OF THE INVENTION

The present invention relates to methods and systems for identifying PVsystem and solar irradiance sensor orientation and tilt based on energyproduction, energy received, simulated energy production, estimatedenergy received, production skew, and energy received skew. The presentinvention relates to systems and methods for detecting orientation andtilt of a PV system based on energy production and simulated energyproduction; for detecting the orientation and tilt of a solar irradiancesensor based on solar irradiance observation and simulated solarirradiance observation; for detecting orientation of a PV system basedon energy production and energy production skew; and for detectingorientation of a solar irradiance sensor based on solar irradianceobservation and solar irradiance observation skew.

According to one embodiment of the present invention, a computerprocessor implemented method of identifying the orientation and tilt ofa renewable energy system is provided, the method comprising the stepsof; providing a set of renewable energy systems having at least tworenewable energy systems each having an orientation and tilt angle pairto provide a set of orientation and tilt known renewable energy systemsin a computer processor; determining environmental conditions for eachof the orientation and tilt known renewable energy systems to provideenvironmental conditions at each orientation and tilt known renewableenergy system in a computer processor; determining simulated productiondata by a computer processor based on the environmental conditions ateach orientation and tilt angle pair to provide simulated productiondata for each orientation and tilt angle pair known renewable energysystem; storing the orientation and tilt angle pair and simulatedproduction data in a computer processor; providing at least oneorientation and tilt unknown renewable energy system in a computerprocessor; calculating by the computer processor a correlation for eachorientation and tilt unknown renewable energy systems production data tothe simulated production data for each orientation and tilt angle pairknown renewable energy systems; setting the orientation and tilt for theorientation and tilt unknown renewable energy systems to the mostcorrelated orientation and tilt known renewable energy systems to becomea correlated simulated orientation and tilt known renewable energysystems that is part of the set of renewable energy systems having atleast two renewable energy systems each having an orientation and tiltangle pair in the computer processor.

According to another embodiment of the present invention, a computerprocessor implemented method of identifying the orientation and tilt ofa solar irradiance sensor, the method comprising the steps of; providinga set of solar irradiance sensors having at least two solar irradiancesensors each having an orientation and tilt angle pair to provide a setof orientation and tilt known solar irradiance sensors in a computerprocessor; determining environmental conditions for each of theorientation and tilt known solar irradiance sensors to provideenvironmental conditions at each orientation and tilt known solarirradiance sensors in a computer processor; determining simulatedproduction data by a computer processor based on the environmentalconditions at each orientation and tilt angle pair to provide simulatedproduction data for each orientation and tilt angle pair known solarirradiance sensor; storing the orientation and tilt angle pair andsimulated production data in a computer processor; providing at leastone orientation and tilt unknown solar irradiance sensor in a computerprocessor; calculating by the computer processor a correlation for eachorientation and tilt unknown solar irradiance sensors production data tothe simulated production data for each orientation and tilt angle pairknown solar irradiance sensors; setting the orientation and tilt for theorientation and tilt unknown solar irradiance sensor to the mostcorrelated orientation and tilt known renewable solar irradiance sensorsto become a correlated simulated orientation and tilt known solarirradiance sensor that is part of the set of set of renewable energysystems having at least two solar irradiance sensors each having anorientation and tilt angle pair in a computer processor.

According to another embodiment of the present invention, a computerprocessor implemented method of identifying the orientation of arenewable energy system, the method comprising the steps of; providingat least one orientation unknown renewable energy system havingproduction data in a computer processor; storing the production data ina computer processor; filtering the production data day by day forfavorable weather conditions by a computer processor to provide filteredproduction data for each filtered day; identifying and saving the startof production, peak of production and end of production for eachfiltered day in a computer processor; calculating a skew of observationfor one of the at least one orientation unknown renewable energy systemsby a computer processor according to the start of production, peak ofproduction and end of production for each filtered day; calculating anorientation for the one of the at least one orientation unknownrenewable energy systems by a computer processor according to the skewof observation; setting the orientation for the one of the at least oneorientation unknown renewable energy systems to become an orientationknown renewable energy system that becomes part of a set of orientationknown renewable energy systems in a computer processor.

According to another embodiment of the present invention, a computerprocessor implemented method of identifying the orientation of a solarirradiance sensor, the method comprising the steps of: providing anorientation-unknown solar irradiance sensor having solar irradiancesensor data in a computer processor; storing the solar irradiance sensordata in a computer processor; filtering the solar irradiance sensor dataday by day for favorable weather conditions by a computer processor toprovide filtered solar irradiance sensor data for each filtered day;identifying and saving the start of production, peak of production andend of production for each filtered day in a computer processor;calculating a skew of observation for each the solar irradiance sensorby a computer processor according to the start of production, peak ofproduction and end of production for each filtered day; calculating theorientation of the solar irradiance sensor by a computer processoraccording to the skew of observation; setting the orientation for theorientation-unknown solar irradiance sensor to become anorientation-known solar irradiance sensor that becomes part of a set oforientation-known solar irradiance sensors in a computer processor.

These and other features, aspects and advantages of the presentinvention will become better understood with reference to the followingdescription and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts the present invention;

FIG. 2 depicts the present invention;

FIG. 3 depicts the present invention;

FIG. 4 depicts the present invention;

FIG. 5 depicts the present invention;

FIG. 6 depicts the present invention;

FIG. 7 depicts the present invention;

FIG. 8 depicts the present invention;

FIG. 9 depicts the present invention;

FIG. 10 depicts the present invention;

FIG. 11 depicts the present invention;

FIG. 12 depicts the present invention; and

FIG. 13 depicts the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The following detailed description is of the best currently contemplatedmodes of carrying out the invention. The description is not to be takenin a limiting sense, but is made merely for the purpose of illustratingthe general principles of the invention, since the scope of theinvention is best defined by the appended claims.

FIGS. 1-5 provide examples of a monitored renewable energy system (morespecifically a photovoltaic array solar panel also referred to herein asa solar photovoltaic system or solar powered system) from whichinformation may be obtained. According to the example shown, there is aserver 10 and at least one monitored renewable energy system (e.g. 102,104, 106, 108, 110, 112) which is provided to a user or consumer. Theremay be at least one data server (10), at least one generation monitoringdevice (16) in communication with the monitored renewable energy system(at premise monitored renewable energy system (30)) and at least onecommunication node (22) in communication with at least one of themonitored renewable energy system (30), the generation monitoring device(16) and the data server (10). It should be understood the data servermay be a single computer, a distributed network of computers, adedicated server, any computer processor implemented device or a networkof computer processor implemented devices, as would be appreciated bythose of skill in the art. The monitored renewable energy system mayhave background constants that are entered into the system during datasetup; populating this part of the data structure is one of the initialsteps to the process. During this time, all required (or potentiallyrequired) background information may be loaded into the system. Thisdata will later be used for system calculations and diagnostics.Background constants may include: (1) Full Calendar with sunrise andsunset according to latitude throughout the year; (2) Insolation or‘incident solar radiation’: This is the actual amount of sunlightfalling on a specific geographical location. There are expected amountsof radiation which will fall on an area each day, as well as an annualfigure. Specific Insolation is calculated as kWh/m2/day. The importanceof this variable is that it can combine several other BackgroundConstants; and (3) Location Functionality. It is envisioned that some ofthis information may be input and some may be determined automatically.The proximity of each system to each other system may be determined, andforms a part of the methods used to determine the geographic average ofthe renewable energy systems. While there are different specific methodsof implementing Location Functionality, generally this relies on a largedatabase of locations which are tied to zones. Because the relationaldistances between the zones are stored within the software, thedistances between any two locations can then be easily and accuratelycalculated.

The term production data refers to any data that is received from therenewable energy system and/or solar irradiance sensor. The energygenerated by each monitored renewable energy system and/or solarirradiance sensor is recorded as production data and the data server maythen determine comparative information based upon at least one of thebackground constant, the diagnostic variable, the system coefficient andthe energy generated to determine a comparative value of the monitoredrenewable energy system. The term comparative value is intended toinclude any value that compares one system to another system or a groupof systems. For example, this may be as simple as an “underperforming”designation when the system's performance is less than another system orgroup of systems performance in terms of power generated.

A sample system may have at least one inverter (14) in communicationwith the monitored renewable energy system (e.g. 50, 30). The inverter(14) is an electronic circuit that converts direct current (DC) toalternating current (AC). There may also be at least one return monitor(18) determining the energy returned to a grid by the at-least onemonitored renewable energy system. At least one background constant maybe determined and saved in the data server(s). The monitored renewableenergy system (e.g. 30, 50) may be at least partially powered by atleast one alternate energy source. There may be at least one generationmonitoring device (e.g. 58), which calculates the energy generated ateach consumer's premises by the monitored renewable energy system (e.g.30, 50); at least one communication node (64) in communication with eachat least one generation monitoring device (e.g. 58); at least one dataserver (10) in communication with communication node (e.g. 64), whereinthe data server(s) (10) accept information from the communication node(e.g. 64) to determine the power generated at a first user's premises(100) and compare the power generated at a first user's premises (100)to Comparative Information obtained from at least two monitoredrenewable energy systems (e.g. 102, 104, 106, 108, 110, 112, 114) todetermine if the first user's monitored renewable energy system (100) iswithin a predetermined deviation from the comparative information. Thismay provide a comparative value. The communication node may be furthercomprising a data storage means for storing usage information. Forexample, the communication node (64) may be a computer with a hard drivethat acts as a data storage means for storing usage information. Thegeneration monitoring device may be selected from the group consistingof pulse meter, temperature meter, electromechanical meter, solid statemeter, flow meter, electric meter, energy meter and watt meter. Theremay also be at least one return monitoring device in communication withthe inverter which calculates the energy returned to a grid by thesystem.

The monitored renewable energy system may be, for example, a solarsystem, solar panel system, photovoltaic, thermal, wind powered,geothermal, hydropower. A secondary energy source (e.g. 52) may be incommunication with and at least partially powering the monitoredrenewable energy system. It should be understood, though, this is onlyfor ancillary power in the event that the renewable energy source (50)is not capable of entirely powering the at premise monitored renewableenergy system.

The generation monitoring device may be any type of meter, by way ofexample, this may include a pulse meter, temperature meter,electromechanical meter, solid state meter, flow meter, electric meter,energy meter and watt meter. An installation will have a communicationnode or hub set up at the location with the system. One of thecommunication nodes may act as a hub. These devices connect to theinternet and send the data collected by the nodes to the Server. Theyhave the following properties: The hub has a web server and connects toa standard internet connection (Ethernet). It does not require acomputer or other device to make this connection. Each hub has its ownunique IP or DNS address. The hub is configured by a web browser. Theweb browser allows the hub to have specific nodes assigned to it. Thisset up feature will allow another hub in the area to be set up with itsown nodes so that all can operate wirelessly without disruption. Also,the hub is able to configure specific aspects of the hub, such as theconnection with the server, data recording and time settings and theability to configure the attached nodes, including their recordingproperties.

Each installation may have two or more Data Nodes. These are typicallyconnected wirelessly to the Hub, and connected directly to theinputs/outputs from the Solar Hot Water system. They communicateconstantly with the Hub, transferring data which the Hub then sends upto the server. They may have the following properties: The firstRequired Node connects to a flow meter attached to the Water Tank thatis connected to the Solar Hot Water system. This Node will operate as apulse meter, ‘clicking’ whenever a unit (either a gallon or a liter) ofhot water passes from the tank. The second Required Node connects toeither the electric panel at the switch for the Hot Water tank'selectric power OR to a flow/other meter for gas/oil to the secondaryheater for the Hot Water tank. The Node may have a data storage meansfor storing flow/usage information. Together, the data gathered fromthese Required Node connections allow the software on the serve toconvert the utilized hot water into an accurate reading of utilizedsolar energy by subtracting the energy required to by the secondaryheating mechanism. The term utilized generation refers to the energygenerated by the at-premise power system, less any energy that has notbeen consumed by the at premise power system (e.g. the energy used toheat water that remains in the tank and is not subsequently used). Notethat the term “at-premise power system” is one type of monitoredrenewable energy system, as claimed. There may also be other Nodes,which may be used to measure other aspects of the system and gain evenmore accurate readings. For example: the temperature of the hot water onan ongoing basis. The system may be monitored from a remote location(such as a computer in a different location).

The components node (100), hub (102) and server (10) make up therequired core components needed to accurately measures the actual usableoutput from a Solar Hot Water (SHW) system. Essentially, the hub (102)connects to multiple nodes (100) which simultaneously measure thesecondary power going into the system along with the hot water goingout. Controlling for any background power requirements (e.g. forpumping), it can measure the usable BTUs created by solar by analyzingthe measurements at the server (104) level.

The renewable energy system may be a solar system, solar panel system,photovoltaic, thermal, wind powered, geothermal, hydropower or any otherrenewable energy system. Also, the terms at-premises, on premises andat-premise are interchangeable and equivalent. Additionally, for thoseinterested in heating and cooling their dwelling via renewable energy,geothermal heat pump systems that tap the constant temperature of theearth, which is around 7 to 15 degrees Celsius a few feet underground,are an option and save money over conventional natural gas andpetroleum-fueled heat approaches.

The method may further comprise the steps of: monitoring the system froma remote location; and monitoring the utilized generation from a remotelocation. The method may comprise the steps of: generating an alert whenthe customer variables are a prescribed percentage different thanhistorical averages. The method may also comprise the steps ofmonitoring and storing the consumer's customer variables and utilizedgeneration.

The present invention provides a computer processor implemented methodof identifying the orientation and tilt of a renewable energy system(e.g. 102, 106, 108), the method comprising the steps of: providing aset of renewable energy systems having at least two renewable energysystems each having an orientation and tilt angle pair to provide a setof orientation and tilt known renewable energy systems in a computerprocessor. The term “computer processor” is intended to include anycomputing device, such as a computer, laptop, smart phone and tabletdevice. The term “orientation and tilt known renewable energy systems”are, just as the name implies, renewable energy systems for which it isknown what the orientation and tilt are. The term “orientation and tiltunknown renewable energy systems” are, just as the name implies,renewable energy systems for which the orientation and tilt is not known(or unknown). There may be the steps of determining environmentalconditions for each of the orientation and tilt known renewable energysystem to provide environmental conditions at each orientation and tiltknown renewable energy system in a computer processor; determiningsimulated production data by a computer processor based on saidenvironmental conditions at each orientation and tilt angle pair toprovide simulated production data for each orientation and tilt anglepair known renewable energy system; storing the orientation and tiltangle pair and simulated production data in a computer processor;providing at least one orientation and tilt unknown renewable energysystem in a computer processor; calculating by the computer processor acorrelation for each orientation and tilt unknown renewable energysystems production data to said simulated production data for eachorientation and tilt angle pair known renewable energy systems; settingthe orientation and tilt for the orientation and tilt unknown renewableenergy systems to the most correlated orientation and tilt knownrenewable energy systems to become a correlated simulated orientationand tilt known renewable energy systems that is part of the set ofrenewable energy systems having at least two renewable energy systemseach having an orientation and tilt angle pair in said computerprocessor.

FIGS. 7-9 depict a triangulation method of correlating according to thepresent invention. As shown in FIG. 7, location known renewable energysystems (700) are marked on an orientation and tilt grid. An orientationand tilt unknown renewable energy system (702) is shown in FIG. 9. Onetriangulation method may be to set the orientation and tilt to thehighest correlated location known system's orientation and tilt. Asshown in FIG. 8, the orientation and tilt pair and production data arestored in a computer processor. FIG. 9 depicts correlating alocation-unknown renewable energy system (702) to at least one locationknown renewable energy system (700). The step of correlating a locationmay be according to Geospatial interpolation methods, included but notlimited to kriging, inverse distance weighting, bilinear interpolation,bicubic interpolation, Barnes interpolation, and spline interpolation(for latitude and longitude).

Correlation is calculated by taking 2 sets of time series data (in ourcase from an unknown system and a known system) and calculatingPearson's correlation coefficient using those datasets. Using a computeralgorithm we calculate correlation from the formula below, where r iscorrelation, X is data from one set, and Y is data from the other.

$r = \frac{\sum\limits_{i = 1}^{n}\;{\left( {X_{i} - \overset{\_}{X}} \right)\left( {Y_{i} - \overset{\_}{Y}} \right)}}{\sqrt{\sum\limits_{i = 1}^{n}\;\left( {X_{i} - \overset{\_}{X}} \right)^{2}}\sqrt{\sum\limits_{i = 1}^{n}\;\left( {Y_{i} - \overset{\_}{Y}} \right)^{2}}}$The correlation based identification logic is for identifying arenewable energy system's orientation and tilt based on the correlationof the system's energy production with observed or simulated energyproduction at a known location. This logic can also identify anenvironmental sensor's orientation and tilt based on the correlation ofthe sensor's observations with observed or simulated environmentalconditions.

Production data could come from, without limitation, PV System (kW orkWh), Solar thermal system (kW or kWh), Concentrated solar power system(kW or kWh) and Wind turbine (kW or kWh). Sensor data could come from,without limitation, Pyranometer (W/m{circumflex over ( )}2 orWh/m{circumflex over ( )}2), Pyrheliometer (W/m{circumflex over ( )}2 orWh/m{circumflex over ( )}2), PV reference cell (W/m{circumflex over( )}2 or Wh/m{circumflex over ( )}2), Radiometer (W/m{circumflex over( )}2 or Wh/m{circumflex over ( )}2), Pyrgeometer (W/m{circumflex over( )}2 or Wh/m{circumflex over ( )}2), Anemometer (mph or m/s). This typeof data consists of a hardware measurement (units listed besidehardware) and a corresponding point in time or time interval, producinga time series of data (multiple time points and data). For example,monitored PV production data is measured every 5 minutes, resulting in a1 day dataset containing 288 measurements and timestamp pairs.

The production data may be simulated production data according toenvironmental conditions at the orientation and tilt pair and the stepof correlating by the computer processor each orientation andtilt-unknown renewable energy system to at least one orientation andtilt-known renewable energy system is according to the orientation andtilt-known renewable energy systems orientation and tilt and thesimulated production data. The environmental conditions at theorientation and tilt pair may be estimated and/or observed.

The renewable energy systems' production data may be selected from thegroup consisting of photovoltaic system production data, solar thermalsystem production data, concentrated solar power system production dataand wind turbine production data.

Another aspect of the present invention provides a computer processorimplemented method of identifying the orientation and tilt of a solarirradiance sensor, the method comprising the steps of; providing a setof solar irradiance sensors having at least two solar irradiance sensorseach having an orientation and tilt angle pair to provide a set oforientation and tilt known solar irradiance sensors in a computerprocessor; determining environmental conditions for each of theorientation and tilt known solar irradiance sensors to provideenvironmental conditions at each orientation and tilt known solarirradiance sensors in a computer processor; determining simulatedproduction data by a computer processor based on said environmentalconditions at each orientation and tilt angle pair to provide simulatedproduction data for each orientation and tilt angle pair known solarirradiance sensor; storing the orientation and tilt angle pair andsimulated production data in a computer processor; providing at leastone orientation and tilt unknown solar irradiance sensor in a computerprocessor; calculating by said computer processor a correlation for eachorientation and tilt unknown solar irradiance sensors production data tosaid simulated production data for each orientation and tilt angle pairknown solar irradiance sensors; setting said orientation and tilt forsaid orientation and tilt unknown solar irradiance sensor to the mostcorrelated orientation and tilt known renewable solar irradiance sensorsto become a correlated simulated orientation and tilt known solarirradiance sensor that is part of the set of set of renewable energysystems having at least two solar irradiance sensors each having anorientation and tilt angle pair in a computer processor. The solarirradiance sensor may be selected from the group consisting ofpyranometer, pyrheliometer and photovoltaic reference cell sensor. Theenvironmental conditions for each of the orientation and tilt knownrenewable energy systems may be estimated and/or observed.

According to another aspect of the present invention, a computerprocessor implemented method of identifying the orientation of arenewable energy system, said method comprising the steps of; providingat least one orientation unknown renewable energy system havingproduction data in a computer processor; storing the production data ina computer processor; filtering said production data day by day forfavorable weather conditions by a computer processor to provide filteredproduction data for each filtered day; identifying and saving the startof production, peak of production and end of production for eachfiltered day in a computer processor; calculating a skew of observationfor one of said at least one orientation unknown renewable energysystems by a computer processor according to the start of production,peak of production and end of production for each filtered day;calculating an orientation for said one of said at least one orientationunknown renewable energy systems by a computer processor according tosaid skew of observation; setting said orientation for said one of saidat least one orientation unknown renewable energy systems to become anorientation known renewable energy system that becomes part of a set oforientation known renewable energy systems in a computer processor. Theproduction data may be simulated production data according toenvironmental conditions at the renewable energy systems location. Theenvironmental conditions may be estimated and/or observed. The renewableenergy system may be selected from the group consisting of photovoltaicsystem, solar thermal system, concentrated solar power system and windturbine.

According to another aspect of the present invention, a computerprocessor implemented method of identifying the orientation of a solarirradiance sensor is provided, the method comprising the steps of:providing an orientation-unknown solar irradiance sensor having solarirradiance sensor data in a computer processor; storing the solarirradiance sensor data in a computer processor; filtering the solarirradiance sensor data day by day for favorable weather conditions by acomputer processor to provide filtered solar irradiance sensor data foreach filtered day; identifying and saving the start of production, peakof production and end of production for each filtered day in a computerprocessor; calculating a skew of observation for each said solarirradiance sensor by a computer processor according to the start ofproduction, peak of production and end of production for each filteredday; calculating the orientation of said solar irradiance sensor by acomputer processor according to said skew of observation; setting saidorientation for the orientation-unknown solar irradiance sensor tobecome an orientation-known solar irradiance sensor that becomes part ofa set of orientation-known solar irradiance sensors in a computerprocessor. The solar irradiance sensor data may be simulated solarirradiance sensor data according to environmental conditions at thelocation of the solar irradiance sensor. The environmental conditionsmay be estimated and/or observed.

There may be observation skew based orientation identification logic.This is logic for identifying a PV system's orientation based on theskew of energy production. This logic can also identify a solarirradiance sensor's orientation based on the skew of observed solarirradiance. FIG. 6 depicts an irradiance map. Note that typically, anirradiance map may provide the amount of solar radiation in kWh/m²/day.Theoretically, hardware oriented due North or South should have evenproduction or solar irradiance measurements in the morning and eveningand deviations from this relationship are caused by changes inorientation.

The Methodology is comprised of the following, background variables,input parameters and logic based on those variables parameters. Theremay be a renewable energy system with unknown or incorrect orientationfeed. This is a feed providing production data and time from a renewableenergy system with an unknown or incorrect orientation. Renewable energysystems include, but are not limited to, solar power systems and windpower systems. Typically, there is a PV SYSTEM production feed. Thisfeed provides data on a PV systems energy production. Variables includeenergy production, time, and location among others.

There may be a solar irradiance sensor with unknown or incorrectorientation feed. This is a feed providing environmental condition dataand time from an environmental sensor with an unknown or incorrectorientation. Environmental sensors include, but are not limited to,solar irradiance sensors, wind sensors, and temperature sensors.Typically, there is a solar irradiance sensor feed. This feed providesdata about the amount of solar irradiance received by a solar irradiancesensor. Variables include solar irradiance observed, time, and locationamong others. There may be an environmental conditions model/feeds.These are models and feeds that provide data on environmental conditionsvital to photovoltaic energy production. This includes, but is notlimited to, solar irradiance sensor feeds, solar irradiance models, windsensor feeds, and temperature sensor feeds. There may also be PV systemproduction models. These are models that simulate the energy productionfor a PV system using environmental condition models/feeds.

Methods for solving search and optimization problems may include, butare not limited to, brute force search, simulated annealing, and greedyalgorithm.

There may be environmental condition models and feeds. These are modelsand feeds that provide data on environmental conditions vital torenewable energy production. This includes, but is not limited to, solarirradiance, wind, and temperature models and feeds.

There may be renewable energy system production models. These are modelsthat simulate the energy production for a variety of renewable energysystems using environmental condition models/feeds. These renewableenergy system models include, but are not limited to, photovoltaic,solar thermal, and wind models.

There may be weather filter logic. This is logic that leverages therelationship between favorable weather and high renewable energyproduction and filters days of production for those days with goodweather.

There may be observation event detection logic. This is empiricallyderived logic for identifying the start, peak, and end of production fora PV system and observed irradiance for a solar irradiance sensor.

There may be correlation based orientation and tilt identificationlogic. This is logic for identifying PV system orientation and tiltbased on the correlation of energy production by systems at the samelocation with similar orientation and tilt. This is logic also foridentifying solar irradiance sensor orientation and tilt based oncorrelation of measured solar irradiance at the same location withsimilar orientation and tilt. This correlation occurs because hardwarewith similar orientation and tilt receive similar solar irradiance.

There may be a correlation based PV orientation and tilt detectionmodel. This is a model that detects orientation and tilt of PV systemsusing correlation based identification logic. The model leverages searchproblem methods, environmental condition models/feeds, and PV systemproduction models in order to simulate production, which is thencorrelated with the PV system production feed to identify orientationand tilt.

There may be a correlation based solar irradiance sensor orientation andtilt detection model. This is a model that detects orientation and tiltof solar irradiance sensors using correlation based identificationlogic. The model leverages search problem methods and environmentalcondition models/feeds in order to estimate solar irradiance, which isthen correlated with the solar irradiance sensor feed to identifyorientation and tilt.

There may be an observation skew based PV orientation detection model.This is a model that detects orientation of PV systems using observationskew based identification logic. The model leverages weather filterlogic and observation event detection logic in order to identifyorientation.

There may be an observation skew based solar irradiance sensororientation detection model. This is a model that detects orientation ofsolar irradiance sensors using observation skew based identificationlogic. The model leverages weather filter logic and observation eventdetection logic in order to identify orientation.

The Correlation Based Renewable Energy System Location IdentificationModel approach identifies the location of a renewable energy system byfinding the systems it is most highly correlated with in terms of energyproduction. The correlation approach works, because renewable energysystems share the same environmental conditions if they are located inthe same area, and they therefore will have similar behavior in terms ofoutput. This correlation holds on a large geographic scale, so we canuse the correlation effect to find neighboring renewable energy systems,and therefore to locate a renewable energy system if we have othersystems with known locations to start with. Additionally with weathercondition data and system production models, “known” locations can besimulated and the aforementioned logic can be applied.

Definition of Variables.

Correlation Based PV System Orientation and Tilt Identification Models.

Definition of Variables

Orientation angle=Angle at which PV system is directed.

Tilt angle=Angle at which PV system is raised.

Environmental conditions=Estimated or observed solar irradiance, windspeed, temperature, and other variables.

PV system production=Observed PV system energy generation.

Simulated PV system production=Simulated PV system energy generationgiven environmental conditions at orientation and tilt pairs.

Model

Search through all orientation and tilt angle pairs.

Estimate or observe environmental conditions at each pair.

Simulate PV system production based on environmental conditions at eachpair.

Calculate correlation of unknown system's production with all simulatedorientation and tilt pairs.

Set unknown system's orientation and tilt to the orientation and tilt ofthe most correlated simulation

Correlation Based Solar Irradiance Sensor Orientation and TiltIdentification Models

Definition of Variables

Orientation angle=Angle at which solar irradiance sensor is directed.

Tilt angle=Angle at which solar irradiance sensor is raised

Environmental conditions=Estimated or observed solar irradiance, windspeed, temperature, and other variables.

Solar irradiance sensor observations=Observed solar irradiancemeasurements.

Simulated solar irradiance sensor observations=Simulated solarirradiance measurements given environmental conditions at orientationand tilt pairs.

Model

Search through all orientation and tilt angle pairs.

Estimate or observe environmental conditions at each pair.

Calculate correlation of unknown sensor's production with all simulatedorientation and tilt pairs.

Set unknown sensor's orientation and tilt to the orientation and tilt ofthe most correlated simulation

Observation Skew Based PV System Orientation Detection Model

Definition of Variables

PV system production=Observed PV system energy generation.

Production start=Start of PV system energy generation.

Production peak=Peak of PV system energy generation.

Production end=End of PV system energy generation.

Time of productions start (TimeOfStart)=Time of start of PV systemenergy generation.

Time of production peak (TimeOfPeak)=Time of start of PV system energygeneration.

Time of production end (TimeOfEnd)=Time of start of PV system energygeneration.

Skew=Time difference between start to peak of production and peak to endof production.

Orientation angle=Angle at which solar irradiance sensor is directed.

Model

Filter PV production data day by day for favorable weather conditions

Identify start, peak, and end of production each filtered day

Calculate skew of observationSkew=abs(median(TimeOfPeak)−median(TimeOfStart))−abs(median(TimeOfPeak)−median(TimeOfEnd))

Calculate orientationOrientation=Skew*0.7+180

Observation Skew Based Solar Irradiance Sensor Orientation DetectionModel

Definition of Variables

Solar irradiance observations=Observed solar irradiance sensormeasurements

Observation start=Start of Solar irradiance sensor observation

Observation peak=Peak of Solar irradiance sensor observation

Observation end=End of Solar irradiance sensor observation

Time of observation start=Time of start of solar irradiance sensorobservation

Time of observation peak (TimeOfPeak)=Time of start of solar irradiancesensor observation

Time of observation end (TimeOfEnd)=Time of start of solar irradiancesensor observation

Skew=Time difference between start to peak of observations and peak toend of observations

Orientation angle=Angle at which solar irradiance sensor is directed

Model

Filter solar irradiance sensor data day by day for favorable weatherconditions

Identify start, peak, and end of irradiance observation each filteredday

Calculate skew of observationSkew=abs(median(TimeOfPeak)−median(TimeOfStart))−abs(median(TimeOfPeak)−median(TimeOfEnd))

Calculate orientationOrientation=Skew*0.7+180

The present invention helps to understand irradiance sensor orientationand tilt and PV system orientation and tilt. It is important to know theorientation and tilt of irradiance sensors as this explains how theirradiance sensor is collecting data, which is required to fullyunderstand the physical solar resource that is being measured.Information on these solar resources can then be applied to solve avariety of problems. Knowing orientation and tilt of a PV systemexplains how the PV system is configured relative to the available solarresources, which is required to fully understand how the PV system isconverting the input solar resource into PV output energy. Theinformation on the PV system's output can then be applied to solve avariety of problems. There are many situations in which verifying theorientation and tilt of a PV system or an irradiance sensor could beimportant. For example, to provide data cleansing of large quantities ofdata and to validate user input. There are also many situations in whichautomatically discovering the orientation and tilt of a PV system orirradiance sensor could be important. For example, to support smart-gridsystems by automatically figuring out the configuration of an irradiancesensor or PV system (because smart-grid assets may not be centrallyregistered, and this information may not be available otherwise). Indata aggregation and re-packaging situations the full registrationinformation may not be available, so the orientation and tilt would needto be automatically added to the data sets. System modeling purposes,where orientation and tilt information may be a critical part of theestimation process (e.g., to forecast system output one may need toassess historical behavior/performance under different weatherconditions, so knowing the orientation and tilt allows one to link theperformance and weather data together).

Orientation and tilt detection of photovoltaic system may be determinedusing system energy production and/or using simulated system energyproduction. The orientation and tilt detection of a solar irradiancesensor may be determined using sensor solar irradiance observationand/or using simulated solar irradiance observation. The orientationdetection of photovoltaic systems may be determined using system energyproduction and/or using skew of production. Orientation detection ofsolar irradiance sensor may be determined using sensor solar irradianceobservation and/or using skew of observation.

Automatic Detection of PV System Configuration

Distributed solar energy is rapidly growing, and with that growth thereis increasing need for software tools to validate data collected on PVsystem configuration. Clean configuration data is the foundation formost fleet performance modeling analytics, but large-scale deployments'data on location, orientation, and tilt are generally subject to humanerror. We will cover a number of techniques to automatically validatelocation, orientation and tilt data.

Location detection: Combining three methodologies to determine locationis often accurate within 10 miles.

Solar noon: By statistically determining a system's solar noon, thesystem's longitude can be extracted from astronomical calculations

Neighbor correlation: System location can be triangulated by correlatingthe system's production with production data from a network of solarprojects with known locations.

Weather & irradiance model simulations: By simulating production usinghistorical weather data and satellite-based irradiance models, aninstalled system's location can be determined by comparing simulated vs.actual production.

Orientation and tilt detection: Combining two methodologies to determineorientation and tilt provides solid data-quality guardrails.

Daily start, peak, and end times: System orientation is indicated by thetime difference between start to peak, and peak to end, of a typicalsystem production profile.

Weather & irradiance model simulations: Orientation and tilt can befound by comparing actual vs. simulated output from a search throughpotential orientation and tilt angles.

It should be understood that the foregoing relates to preferredembodiments of the invention and that modifications may be made withoutdeparting from the spirit and scope of the invention as set forth in thefollowing claims.

The invention claimed is:
 1. A computer processor implemented method ofidentifying an orientation and a tilt angle of a renewable energysystem, comprising: providing at least two renewable energy systems eachhaving a known orientation and a known tilt angle pair to define a setof orientation and tilt known renewable energy systems in a computerprocessor, wherein the at least two renewable energy systems arephotovoltaic systems; determining environmental conditions for each ofthe orientation and tilt known renewable energy systems to provideenvironmental conditions at each orientation and tilt known renewableenergy system in a computer processor; determining estimated productiondata by a computer processor based on the environmental conditions ateach known orientation and known tilt angle pair to provide estimatedproduction data for each orientation and tilt known renewable energysystem; storing the estimated production data of the known orientationand known tilt angle pair in a computer processor; providing anorientation and tilt unknown renewable energy system that exists in acomputer processor, the orientation and tilt unknown renewable energysystem having production data, wherein the orientation and tilt unknownrenewable energy system is a photovoltaic system; calculating by thecomputer processor a correlation for the production data of theorientation and tilt unknown renewable energy system to the estimatedproduction data for each of the orientation and tilt known renewableenergy systems; and setting, in the computer processor, the orientationand tilt angle for the orientation and tilt unknown renewable energysystem to the orientation and tilt angle of the most correlatedorientation and tilt known renewable energy system.
 2. A method as inclaim 1, wherein the environmental conditions for each of theorientation and tilt known renewable energy systems are estimated.
 3. Amethod as in claim 1, wherein the environmental conditions for each ofthe orientation and tilt known renewable energy systems are observed. 4.A computer processor implemented method of identifying an orientationand a tilt angle of a solar irradiance sensor, comprising: providing atleast two solar irradiance sensors each having a known orientation and aknown tilt angle pair to define a set of orientation and tilt knownsolar irradiance sensors in a computer processor; determiningenvironmental conditions for each of the orientation and tilt knownsolar irradiance sensors to provide environmental conditions at each ofthe orientation and tilt known solar irradiance sensors in a computerprocessor; determining estimated production data by a computer processorbased on the environmental conditions at each known orientation andknown tilt to provide estimated production data for each orientation andtilt known solar irradiance sensor; storing the estimated productiondata of the known orientation and known tilt angle pair in a computerprocessor; providing an existing solar irradiance sensor having anunknown orientation and an unknown tilt angle in a computer processor,the orientation and tilt unknown solar irradiance sensor havingproduction data; calculating by the computer processor a correlation forthe production data of the orientation and tilt unknown solar irradiancesensor to the estimated production data for each of the orientation andtilt known solar irradiance sensors; and setting, in the computerprocessor, the orientation and tilt angle for the orientation and tiltunknown solar irradiance sensor to the orientation and tilt angle of themost correlated orientation and tilt known solar irradiance sensor.
 5. Amethod as in claim 4, wherein the solar irradiance sensor is selectedfrom the group consisting of pyranometer, pyrheliometer and photovoltaicreference cell sensor.
 6. A method as in claim 5, wherein theenvironmental conditions for each of the orientation and tilt knownsolar irradiance sensors are estimated.
 7. A method as in claim 5,wherein the environmental conditions for each of the orientation andtilt known solar irradiance sensors are observed.
 8. A method as inclaim 4, wherein the orientation and tilt unknown solar irradiancesensor becomes an orientation and tilt known solar irradiance sensorafter setting the orientation and tilt angle to the orientation and tiltangle of the most correlated orientation and tilt known solar irradiancesensor.
 9. A method as in claim 1, wherein the environmental conditionsfor each of the orientation and tilt known renewable energy systemsinclude one or more of solar irradiance, wind speed and temperature. 10.A method as in claim 1, wherein the orientation and tilt unknownrenewable energy system becomes an orientation and tilt known renewableenergy system after setting the orientation and tilt angle to theorientation and tilt angle of the most correlated orientation and tiltknown renewable energy system.
 11. A computer processor implementedmethod of identifying an orientation and a tilt angle of an existingrenewable energy system, comprising: providing an existing renewableenergy system with an orientation and a tilt angle that are unknown, theexisting renewable energy system having production data, wherein theexisting renewable energy system is a photovoltaic system; providing aset of renewable energy systems that each have a known orientation and aknown tilt angle pair, each of the renewable energy systems in the sethaving production data, wherein each of the renewable energy systems inthe set is a photovoltaic system; calculating a correlation between theproduction data of the existing renewable energy system and theproduction data for each of the renewable energy systems in the set; andsetting the orientation and tilt angle of the existing renewable energysystem to the orientation and tilt angle of the most correlatedrenewable energy system in the set.
 12. The method of claim 11, whereinthe production data of the renewable energy systems in the set issimulated.
 13. The method of claim 12, wherein the production data issimulated based on estimated environmental conditions for the renewableenergy systems in the set.
 14. The method of claim 12, wherein theproduction data is simulated based on observed environmental conditionsfor the renewable energy systems in the set.
 15. The method of claim 11,wherein the production data of the renewable energy systems in the setis observed.
 16. The method of claim 11, wherein the orientation andtilt angle of the existing renewable energy system is set to theorientation and tilt angle of the renewable energy system with theproduction data with the highest correlation to the production data ofthe existing renewable energy system.
 17. The method of claim 11,wherein calculating the correlation includes calculating Pearson'scorrelation coefficient using the production data of the existingrenewable energy system and the production data for each of therenewable energy systems in the set.